Practical guide to implementing AWS S3 Tables for modern data lake architectures and analytics

What you'll learn
- What AWS S3 Tables are and why they matter in modern analytics
- How S3 Tables differ from traditional S3 data lakes and Redshift data warehouses
- The role of Apache Iceberg and how it enables schema evolution, time travel, and ACID transactions
- How to create and query S3 Tables using Sagemaker Lakehouse, Glue Iceberg Endpoint, S3 Tables Iceberg Endpoint and Catalog
- How to secure your tables using IAM, Resource Policies and AWS Lake Formation
- Best practices for managing metadata, compaction, and snapshot cleanup

Requirements
- Basic Understanding of AWS Technologies - AWS S3, AWS IAM
- Awareness on Data Engineering - Data Lake, Big Data, Open Table Format, ETL
- Awareness on Data Engineering Technologies- Apache Spark, Apache Iceberg, PyIceberg

Description
Welcome to "AWS S3 Tables for Beginners: Foundation of Modern Analytics"— your complete introduction to one of AWS's newest and most powerful analytics services.

This course is designed to help you understand, set up, and work withAWS S3 Tables, a modern, open table format built onApache Iceberg, that bringsdata warehouse reliabilitytodata lakes.

By the end of this course, you'll have the skills to create, query, and manage S3 Tables efficiently — and understand how they fit into the broader AWS analytics ecosystem alongsideAthena, Redshift, Glue, and Lake Formation.

What You'll Learn

What AWS S3 Tables are and why they matter in modern analytics

How S3 Tables differ from traditional S3 data lakes and Redshift data warehouses

The role ofApache Icebergand how it enables schema evolution, time travel, and ACID transactions

How tocreate and query S3 Tablesusing Sagemaker Lakehouse, Glue Iceberg Endpoint, S3 Tables Iceberg Endpoint and Catalog

How tosecure your tablesusing IAM, Resource Policies and AWS Lake Formation

Best practices formanaging metadata, compaction, and snapshot cleanup

Hands-on examples of building and accessing S3 Tables via catalogs and APIs

Course Structure

Introduction– Understand the evolution from data lakes to lakehouses and where S3 Tables fit in

Getting Started– Learn how to enable and create S3 Tables in your AWS account

Accessing S3 Tables– Query data using Sagemaker Lakehouse, Glue Iceberg Endpoint, S3 Tables Iceberg Endpoint and Catalog

Securing S3 Tables– Apply IAM level, Resource level and fine-grained access at table, column, and row-level using Lake Formation

Managing S3 Tables– Explore maintenance tasks such as compaction, schema evolution, and metadata optimization

Conclusion– Recap and understand how S3 Tables simplify and modernize data analytics on AWS

Who This Course Is For

Data engineers, analysts, and cloud architects exploring AWS analytics services

Professionals transitioning from traditional data warehouses to data lakes or lakehouses

Anyone who wants to understandhow open table formats like Icebergare changing cloud data management

Prerequisites

Basic understanding of AWS (S3, IAM, Athena, or Redshift) is helpful but not required

Awareness on Data Engineering Concepts and Technologies including Data Lake, Open Table Format, Apache Iceberg, Apache Spark and PyIceberg

Why Take This Course

AWS S3 Tables simplify the complexity of self-managed data lakes by automating maintenance, improving query performance, and ensuring data consistency.With this course, you'll gain bothconceptual clarityandhands-on understandingof how to build reliable, scalable, and open analytics systems on AWS.

Who this course is for:
- Data engineers, analysts, and cloud architects exploring AWS analytics services
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